OBJECTIVES: The stringent process of prior authorization (PA) has negative impacts on timeliness of treatments acting as an administrative burden for the payers, providers, and care delays for patients.

This study focuses on deployment of PA analytics for a specialty product using advanced clinical, predictive, and prescriptive analytics and generating payer/provider insights for solving the challenge of drug abandonment and driving outcomes for patients.

METHODS: Deep clinical knowledge base along with natural language processing (NLP) and machine learning (ML)-driven analytics tools were used in processing huge volumes of unstructured patient PA data from the patient hub. Case-by-case retrospective analysis of 5000 patients’ PA submissions in severe and critical autoimmune conditions in Neurology, Rheumatology, Nephrology, Pulmonology, and Ophthalmology across various payers and providers was performed to generate insights through patient clinical profiling and mapping payer and provider behaviour modelling. The unstructured medical records or case forms were converted to comprehensible structured data using NLP-based clinical algorithms to determine cause and effect of prior rejection instances. The structured data analytics helped ensure generation and interpretation of patient clinical profiling insights, cost-benefit analysis and payer behaviour mapping insights to derive recommendations that the pharma client leveraged to have focused negotiations with payers on better drug coverage decisions and PA approvals.

RESULTS: The medical necessity and payer modelling insights, and case-by-case actionable recommendations helped a leading US-based pharma enterprise in recording a 32% improvement in PA approvals through reduction in unwarranted rejections of clinically valid PA submissions.

The provider modelling insights helped the pharmaceutical enterprise in provider education to close the gaps in the submission of clinical evidence for better PA submissions, achieving about 10% reduction in PA rejections.

CONCLUSIONS: This NLP driven analytics solution enabled the pharma enterprise to drive outcomes by ensuring faster patient access to drugs.